Package: drjacoby 1.5.4

Bob Verity

drjacoby: Flexible Markov Chain Monte Carlo via Reparameterization

drjacoby is an R package for performing Bayesian inference via Markov chain monte carlo (MCMC). In addition to being highly flexible it implements some advanced techniques that can improve mixing in tricky situations.

Authors:Bob Verity [aut, cre], Pete Winskill [aut]

drjacoby_1.5.4.tar.gz
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drjacoby_1.5.4.tgz(r-4.6-x86_64)drjacoby_1.5.4.tgz(r-4.6-arm64)drjacoby_1.5.4.tgz(r-4.5-x86_64)drjacoby_1.5.4.tgz(r-4.5-arm64)
drjacoby_1.5.4.tar.gz(r-4.7-arm64)drjacoby_1.5.4.tar.gz(r-4.7-x86_64)drjacoby_1.5.4.tar.gz(r-4.6-arm64)drjacoby_1.5.4.tar.gz(r-4.6-x86_64)
drjacoby_1.5.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
drjacoby/json (API)

# Install 'drjacoby' in R:
install.packages('drjacoby', repos = c('https://mrc-ide.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mrc-ide/drjacoby/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

5.70 score 13 stars 77 scripts 14 exports 61 dependencies

Last updated from:edfea6339e (on master). Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK175
linux-devel-x86_64OK184
source / vignettesOK333
linux-release-arm64OK176
linux-release-x86_64OK186
macos-release-arm64OK110
macos-release-x86_64OK325
macos-oldrel-arm64OK123
macos-oldrel-x86_64OK249
windows-develOK201
windows-releaseOK204
windows-oldrelOK191
wasm-releaseOK132

Exports:check_drjacoby_loadedcpp_templatedefine_paramsplot_autocorrelationplot_cor_matplot_credibleplot_densityplot_mc_acceptanceplot_pairsplot_rung_loglikeplot_scatterplot_tracerun_mcmcsample_chains

Dependencies:askpassclicliprcodacowplotcpp11crayoncredentialscurldescdplyrfarverforcatsfsgenericsgertGGallyggplot2ggstatsgitcredsgluegtablehmshttr2iniisobandjsonlitelabelinglatticelifecyclemagrittropensslpatchworkpillarpkgconfigprettyunitsprogresspurrrR6rappdirsRColorBrewerRcpprlangrprojrootrstudioapiS7scalesstringistringrsystibbletidyrtidyselectusethisutf8vctrsviridisLitewhiskerwithryamlzip

Basic Example
Setup | Running the MCMC | Exploring outputs and checking MCMC performance | Using C++ functions
Rendered fromexample.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2019-05-03

Double well
Model | Single temperature rung (no Metropolis coupling) | Multiple temperature rungs
Rendered fromchecks_double_well.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2021-09-08

Getting Model Fits
A simple model of population growth
Rendered fromgetting_model_fits.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2022-02-10

Installing drjacoby
Installing Rcpp | Installing and loading drjacoby
Rendered frominstallation.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2019-05-02

Parallel Tempering
Setup | Running the MCMC | How many rungs to use?
Rendered frommetropolis_coupling.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2019-05-22

Running in Parallel
Setup | Running multiple chains | Running multiple chains using C++ log likelihood or log prior functions
Rendered fromparallel.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2019-05-22

Using Likelihood Blocks
Problem motivation | Defining blocks | The Likelihood
Rendered fromblocks.Rmdusingknitr::rmarkdown

Last update: 2022-02-10
Started: 2021-01-08

Multilevel example with blocks
Model | MCMC | Plots
Rendered fromchecks_multilevel_blocks.Rmdusingknitr::rmarkdown

Last update: 2021-09-08
Started: 2021-09-08

Normal model
Model | MCMC | Posterior plots
Rendered fromchecks_normal_model.Rmdusingknitr::rmarkdown

Last update: 2021-09-08
Started: 2021-09-08

Return prior
Model | Run MCMC | Plots
Rendered fromchecks_return_prior.Rmdusingknitr::rmarkdown

Last update: 2021-09-08
Started: 2021-09-08